Data/hora: 01/06/2024 06:32:08

Provedor de dados: 	5	
País: 	France	
Título: 	Wave spectrum retrieval from airborne sunglitter images	
Autores: 	Yurovskaya, Maria; Rascle, Nicolas; Kudryavtsev, Vladimir; Chapron, Bertrand; Marie, Louis; Molemaker, Jeroen.	
Data: 	2018-11	
Ano: 	2018	
Palavras-chave: 	Sunglitter; Sea surface waves; Directional wave spectrum; Aerial photography; Field measurements; Remote sensing observations; High resolution; Drone.	
Resumo: 	Reconstruction and evolution of two-dimensional spectra of surface waves in the Gulf of Mexico are derived from airborne sun-glitter imagery. As the proposed method is based on a linear transfer function deduced from the shape of the sunglitter brightness, the absolute wavenumber elevation spectrum does not require any additional assumption or information about sky brightness, wind or wave energy. The detailed description of the airborne image processing method is given. As demonstrated, retrieved spectra agree well with nearby NDBC buoy data, both for spectrum shape, level and energy angular distribution. The 180-degree wave direction ambiguity, inherent to image-derived spectra, is eliminated by using cross-correlation analysis between two consecutive images. A case study corresponding to the spectral evolution with increasing distance from shore in slanting fetch conditions is then considered. Energy level and peak position transformation are consistent with established approximations and laws of wind-sea development. The technical requirements (flight altitude, image resolution, view angles, etc) and applicability of the suggested methodology are also discussed. These results demonstrate the potential efficiency of high resolution sea state monitoring from drones or light aircrafts using sunglitter imagery.	
Tipo: 	Text	
Idioma: 	Inglês	
Identificador: 	https://archimer.ifremer.fr/doc/00465/57712/60174.pdf

DOI:10.1016/j.rse.2018.07.026

https://archimer.ifremer.fr/doc/00465/57712/	
Editor: 	Elsevier Science Inc	
Formato: 	application/pdf	
Fonte: 	Remote Sensing Of Environment (0034-4257) (Elsevier Science Inc), 2018-11 , Vol. 217 , P. 61-71	
Fonte: 	info:eu-repo/semantics/openAccess

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